Systems and methods for positron emission tomography

ABSTRACT

The disclosure relates to a system and method for reconstructing a PET image. The method may include: obtaining PET data relating to an object collected by a plurality of detector units; determining functional status of the plurality of detector units; generating reconstruction data based on the functional status of the respective detector units and the PET data; and reconstructing a PET image based on the reconstruction data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/815,825, filed on Nov. 17, 2017, which claims priority of ChineseApplication No. 201711042643.2 filed on Oct. 30, 2017, the entirecontents of which is hereby incorporated by reference.

TECHNICAL FIELD

The application generally relates to a system and method for positronemission tomography (PET), and more specifically relates to PET imagereconstruction based on detector unit working status.

BACKGROUND

Positron emission tomography (PET) is a nuclear medicine functionalimaging technique that is used to observe metabolic processes in a body.A PET imaging system may include numerous detector units for detectingradiation events originating from the body. The failure of some detectorunits may affect acquired PET data and therefore a resultant PET image.

SUMMARY

In a first aspect of the present disclosure, a method for reconstructinga PET image is provided. The method may be implemented on at least onedevice each of which has at least one processor and storage. The methodmay include one or more of the following operations. PET data relatingto an object collected by a plurality of detector units may be obtained.Functional status of the plurality of detector units may be determined.Reconstruction data may be generated based on the functional status ofthe respective detector units and the PET data. A PET image may bereconstructed based on the reconstruction data.

In some embodiments, a detector unit of the plurality of detector unitsmay include one or more detector subunits.

In some embodiments, the reconstruction data may include a portion ofthe PET data that is collected by a first detector unit and a seconddetector unit of the plurality of detector units, and a unit differencebetween the first detector unit and the second detector unit may be lessthan a threshold.

In some embodiments, the method may include one or more of the followingoperations. The plurality of detector units may be divided into a firstgroup and a second group based on the functional status of therespective detector units, wherein the functional status of the detectorunits in the first group is positive, and the functional status of thedetector units in the second group is negative. The reconstruction datamay be generated based on the PET data collected by the detector unitsin the first group.

In some embodiments, the detector units in the first group are locatedtogether and not spatially separated by a detector unit of the secondgroup.

In some embodiments, the method may include one or more of the followingoperations. The detector units in the first group may be divided into afirst subgroup and a second subgroup, wherein the detector units in eachof the first subgroup and the second subgroup are located together andnot spatially separated by a detector unit of the second group, and thenumber of detector units in the first subgroup is larger than the numberof detector units in the second subgroup. The reconstruction data may begenerated based on the PET data collected by the detector units in thefirst subgroup.

In some embodiments, when a detector unit of the plurality of detectorunits includes a plurality of detector subunits, the method may includeone or more of the following operations. For each detector unit of theplurality of detector units, functional status of the respectivedetector subunits of a detector unit may be determined. Functionalstatus of the detector unit may be determined based on the functionalstatus of the plurality of detector subunits of the detector unit.

In a second aspect of the present disclosure, a system forreconstructing a PET image is provided. The system may include at leastone storage medium and at least one processor. The at least one storagemedium may include a set of instructions. The at least one processor maybe configured to communicate with the at least one storage medium,wherein when executing the set of instructions, the system is configuredto perform one or more of the following operations. PET data relating toan object collected by a plurality of detector units may be obtained.Functional status of the plurality of detector units may be determined.Reconstruction data may be generated based on the functional status ofthe respective detector units and the PET data. A PET image may bereconstructed based on the reconstruction data.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1A is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 1B is a block diagram illustrating a process engine according tosome embodiments of the present disclosure;

FIG. 2 is a block diagram illustrating exemplary hardware and softwarecomponents of a computing device according to some embodiments of thepresent disclosure;

FIG. 3A is a schematic diagram illustrating a detector subunit accordingto some embodiments of the present disclosure;

FIG. 3B is a schematic diagram illustrating a detector unit according tosome embodiments of the present disclosure;

FIG. 3C is a schematic diagram illustrating a PET scanner according tosome embodiments of the present disclosure;

FIG. 4 is a block diagram of an exemplary processing module according tosome embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process ofreconstructing a PET image according to some embodiments of the presentdisclosure;

FIG. 6 is a flowchart illustrating an exemplary process of determiningreconstruction data according to some embodiments of the presentdisclosure;

FIG. 7A is a schematic diagram illustrating a PET scanner with anon-functional detector unit;

FIG. 7B is an exemplary illustration of coincident events of the PETscanner illustrated in FIG. 7A;

FIG. 8A is a schematic diagram illustrating a PET scanner withnon-functional detector units; and

FIG. 8B is an exemplary illustration of coincident events of the PETscanner illustrated in FIG. 8A.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “unit,” “module,” and/or“block” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by other expression if theyachieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 202 as illustrated in FIG. 2) may beprovided on a computer readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in a firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedof connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and components for non-invasive imaging,such as for disease diagnostic or research purposes. The imaging systemmay find its applications in different fields such as medicine orindustry. For example, the imaging system may be used in internalinspection including, for example, tumor metabolism, brain function,heart function, or the like, or any combination thereof.

The following description is provided to help better understanding PETimage reconstruction methods or systems. The term “image” used in thisdisclosure may refer to a 2D image, a 3D image, a 4D image, or anyrelated image data (e.g., the PET data, projection data corresponding tothe PET data). The image data may correspond to a distribution of PETtracers within the subject (e.g., a patient) or a coincidencedistribution of the plurality of voxels within the subject representedin the form of, e.g., a sinogram. As used herein, a PET tracer may referto a substance that may undergo certain changes under the influence ofan activity or functionality within the subject, whose activity and/orfunctionality are to be visualized and/or studied. This is not intendedto limit the scope the present disclosure. For persons having ordinaryskills in the art, a certain amount of variations, changes, and/ormodifications may be deducted under guidance of the present disclosure.Those variations, changes, and/or modifications do not depart from thescope of the present disclosure.

FIG. 1A is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. In someembodiments, the imaging system 100 may be a single-modality system or amulti-modality system. Exemplary multi-modality imaging system mayinclude a computed tomography-positron emission tomography (CT-PET)system, a magnetic resonance-positron emission tomography (MR-PET)system, etc. In some embodiments, the imaging system 100 may includemodules and/or components for performing imaging and/or relatedanalysis. Merely by way of example, as illustrate in FIG. 1A, theimaging system 100 may include an imaging section 110, a network 120,one or more terminals 130, a processing engine 140, and storage 150; theimaging section 110 may include a PET scanner 112. The components in theimaging system 100 may be connected in one or more of various ways.Merely by way of example, the imaging section 110 may be connected tothe processing engine 140 through the network 120. As another example,the imaging section 110 may be connected to the processing engine 140directly as illustrated in FIG. 1A. As a further example, one or more ofthe terminal 130 may be connected to another component of the imagingsystem 100 (e.g., the processing engine 140) via the network 120 asillustrated in FIG. 1A. As still a further example, at least oneterminal 130 may be connected to the processing engine 140 directly asillustrated by the dotted arrow in FIG. 1A. As still a further example,the storage 150 may be connected to another component of the imagingsystem 100 (e.g., the processing engine 140) directly as illustrated inFIG. 1A, or through the network 120.

The PET scanner 112 may include a detection region 113, a table 114, andone or more detector units. An individual detector unit may furtherinclude one or more detector subunits. The one or more detector subunitsmay include a plurality of PET detectors. Detailed descriptions aboutthe PET detector, the detector subunit, and the detector unit may befound in FIG. 3A, FIG. 3B, and FIG. 3C, and descriptions thereof. Theplurality of detectors may detect radiation events of photons emittedfrom the detection region 113. The table 114 may transport a scan objectinto and out of the detection region 113, and/or facilitate thepositioning of the scan object in the detection region 113.

In some embodiments, a CT scanner 111 may be added to the imaging system100 (e.g., as a part of the imaging section 110), and the imaging systemmay be a multi-modality imaging system. In some embodiments, the PETscanner 112 and CT scanner 111 may be installed separately on a gantryso that the PET scanner 112 does not interfere with the operation of theCT scanner 111. The CT scanner 111 may be a spiral CT, an electron beamCT, an energy spectrum CT, etc. In some embodiments, the spiral CT maybe a multi-slice spiral CT or a multi-row spiral CT. For example, thespiral CT may be an 8 slices spiral CT scanner.

During an exemplary CT-PET scan, a scan object may be supported by thetable 114 and moved into the detection region 113. CT detectors (notshown in the figure) may detect radiation events of X-rays in thedetection region 113. After the CT scan, the scan object may thenundergo a PET scan. After a reconstruction of a CT image based on datafrom the CT scan and a reconstruction of a PET image based on data fromthe PET scan, the multi-modality imaging system 100 may create a fusedimage that includes the PET image spatially registered to the CT image.

In some embodiments, the PET scan may be implemented by scanning one ormore scan regions of the scan object. The one or more scan regions maybe generated by dividing a volume of interest of the scan object intoone or more parts. In some embodiments, the volume of interest of thescan object may be the entire volume of the scan object. In someembodiments, the volume of interest of the scan object may be a portionof the scan object. A scan region may correspond to a portion of a tableon which the scan object is placed during the PET scan. By moving thetable 114 into the detection region 113 along the z axis, each of theone or more scan regions may be scanned. PET data of respective scanregions may then be obtained. At least two scan regions of the one ormore scan regions may at least partially overlap. In some embodiments,the one or more scan regions may completely cover the volume of interestthe scan object. A PET sub-image may be generated based on PET data of ascan region. The PET image of the scan object may be obtained bystitching one or more PET sub-images of the one or more scan regions.The PET image of the scan object may also be directly obtained based onthe entire PET data of the one or more scan regions.

As used in the present disclosure, the PET scanner may include aplurality of detector units, and each of the plurality of detector unitsmay be span a certain width along the z axis. In some embodiments, ascan region may correspond to at least one detector unit. When adetector unit is determined as a non-functional (i.e., in an abnormalworking status) detector unit, PET data relating to a scan regioncorresponding to the non-functional detector unit may be inaccurate. Insome embodiments, the PET data relating to the scan region correspondingto the non-functional detector unit may be excluded from furtherprocessing. In some embodiments, the PET data relating to the scanregion corresponding to the non-functional detector unit may be replacedby performing a re-scan of the scan region. For example, by moving thetable 114 or the PET scanner 112 along the z axis, the scan regioncorresponding to the non-functional detector unit may be moved to aposition that corresponds to a functional (i.e., in a normal workingstatus) detector unit. The scan region may then be re-scanned when it ispositioned corresponding to at least one functional detector unit.Accurate PET data relating to the scan region may be obtained, and mayreplace the PET data relating to the scan region obtained in theprevious PET scan. The PET image of the scan object may be obtainedbased on PET data relating to the one or more scan regions that iscollected by function detector units (or detector subunits).

The network 120 may include any suitable network that can facilitateexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging section 110 (e.g.,the CT scanner 111, the PET scanner 112, etc.), the terminal 130, theprocessing engine 140, the storage 150, etc., may communicateinformation and/or data with one or more other components of the imagingsystem 100 via the network 120. For example, the processing engine 140may obtain data from the imaging section 110 via the network 120. Asanother example, the processing engine 140 may obtain user instructionsfrom the terminal 130 via the network 120. The network 120 may be and/orinclude a public network (e.g., the Internet), a private network (e.g.,a local area network (LAN), a wide area network (WAN)), etc.), a wirednetwork (e.g., an Ethernet network), a wireless network (e.g., an 802.11network, a Wi-Fi network, etc.), a cellular network (e.g., a Long TermEvolution (LTE) network), a frame relay network, a virtual privatenetwork (“VPN”), a satellite network, a telephone network, routers,hubs, switches, server computers, and/or any combination thereof. Merelyby way of example, the network 120 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network120 may include one or more network access points. For example, thenetwork 120 may include wired and/or wireless network access points suchas base stations and/or internet exchange points through which one ormore components of the multi-modality imaging system 110 may beconnected to the network 120 to exchange data and/or information.

The terminal(s) 130 may include a mobile device 131, a tablet computer132, a laptop computer 133, or the like, or any combination thereof. Insome embodiments, the mobile device 131 may include a smart home device,a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistance (PDA),a gaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 130 may be part of the processing engine 140.

The processing engine 140 may process data and/or information obtainedfrom the PET scanner 112, the terminal 130, and/or the storage 150. Insome embodiments, the processing engine 140 may be a computer, a userconsole, a single server or a server group, etc. The server group may becentralized or distributed. In some embodiments, the processing engine140 may be local or remote. For example, the processing engine 140 mayaccess information and/or data stored in the PET scanner 112, theterminal 130, and/or the storage 150 via the network 120. As anotherexample, the processing engine 140 may be directly connected to the PETscanner 112, the terminal 130 and/or the storage 150 to access storedinformation and/or data. In some embodiments, the processing engine 140may be implemented on a cloud platform. Merely by way of example, thecloud platform may include a private cloud, a public cloud, a hybridcloud, a community cloud, a distributed cloud, an inter-cloud, amulti-cloud, or the like, or any combination thereof. In someembodiments, the processing engine 140 may be implemented on a computingdevice 200 having one or more components as illustrated in FIG. 2.

The storage 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage 150 may store dataobtained from the terminal 130 and/or the processing engine 140. In someembodiments, the storage 150 may store data and/or instructions that theprocessing engine 140 may execute or use to perform exemplary methodsdescribed in the present disclosure. In some embodiments, the storage150 may include mass storage, removable storage, volatile read-and-writememory, read-only memory (ROM), or the like, or any combination thereof.Exemplary mass storage may include a magnetic disk, an optical disk, asolid-state drive, etc. Exemplary removable storage may include a flashdrive, a floppy disk, an optical disk, a memory card, a zip disk, amagnetic tape, etc. Exemplary volatile read-and-write memory may includea random access memory (RAM). Exemplary RAM may include a dynamic RAM(DRAM), a double date rate synchronous dynamic RAM (DDR SDRAM), a staticRAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM),etc. Exemplary ROM may include a mask ROM (MROM), a programmable ROM(PROM), an erasable programmable ROM (EPROM), an electrically erasableprogrammable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digitalversatile disk ROM, etc. In some embodiments, the storage 150 may beimplemented on a cloud platform. Merely by way of example, the cloudplatform may include a private cloud, a public cloud, a hybrid cloud, acommunity cloud, a distributed cloud, an inter-cloud, a multi-cloud, orthe like, or any combination thereof.

In some embodiments, the storage 150 may be connected to the network 120to communicate with one or more other components in the imaging system100 (e.g., the processing engine 140, the terminal 130, etc.). One ormore components in the imaging system 100 may access the data orinstructions stored in the storage 150 via the network 120. In someembodiments, the storage 150 may be directly connected to or communicatewith one or more other components in the imaging system 100 (e.g., theprocessing engine 140, the terminal 130, etc.). In some embodiments, thestorage 150 may be part of the processing engine 140.

It should be noted that the above description of the imaging system 100is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the assemblyand/or function of the imaging system 100 may be varied or changedaccording to specific implementation scenarios. Merely by way ofexample, some other components may be added into the imaging system 100,such as a subject positioning module, a gradient amplifier module, andother devices or modules.

FIG. 1B is a block diagram illustrating a processing engine according tosome embodiments of the present disclosure. In some embodiments, theprocessing engine 140 may be implemented on a computing device 200 asillustrated in FIG. 2. As illustrated in FIG. 1B, the process engine 140may include an acquisition module 141, a control module 142, a storagemodule 143, and a processing module 144.

The acquisition module 141 may acquire or receive CT data and/or PETdata. Merely by way of example with reference to a PET scanner 112, theacquisition module 141 may acquire or receive PET data. In someembodiments, during a PET scan or analysis, PET tracer (also referred toas “PET tracer molecules”) are first introduced into a scan objectbefore an imaging process begins. During the PET scan, the PET tracermolecules may emit positrons, namely the antiparticles of electrons. Apositron has the same mass as and the opposite electrical charge withrespect to an electron, and it may undergo an annihilation (alsoreferred to as an “annihilation event”) with an electron (that maynaturally exist in abundance within the scan object) as the twoparticles collide. An electron-positron annihilation may result in two511 keV gamma photons, which, upon their own generation, begin to travelin (substantially) opposite directions with respect to one another. Thisproperty of collinearity of the two gamma photons may be used to definea line-of-sight of the event without the need for physical collimation.The line connecting the two gamma photons may be referred to as a “lineof response (LOR).” The acquisition module 141 may obtain trajectoryand/or information of gamma photons (also referred to as the “PETdata”). The PET data may include a list of single events, coincidentevents, annihilation events, transverse and longitudinal positions ofLORs, or the like, or a combination thereof. The PET data may be used todetermine the distribution of the PET tracer molecules in the imagedomain and/or the coincidence distribution of voxels in the sinogramcoordinate system. In some embodiments, the acquisition module 141 mayinclude different zones to acquire PET data collected by a plurality ofdetector units, respectively.

In some embodiments, the PET tracer may include carbon (11C), nitrogen(13N), oxygen (15O), fluorine (18F), or the like, or a combinationthereof. In some embodiments, for a single photon emission computedtomography (SPECT) system, a SPECT tracer may be introduced into a scanobject. The SPECT tracer may include technetium-99m, iodine-123,indium-111, iodine-131, or the like, or a combination thereof.Accordingly, in some embodiments, the PET tracer or SPECT tracer of thepresent disclosure may be organic compounds containing one or more ofsuch isotopes. These tracers are either similar to naturally occurringsubstances or otherwise capable of interacting with the functionality oractivity of interest within the scan object. Hence, distributionalinformation of the tracer may be used as an indicator of the scan objectfunctionality.

The control module 142 may generate a control parameter for controllingthe acquisition module 141, the storage module 143, and/or theprocessing module 144. For example, the control module 142 may controlthe acquisition module 141 as to whether to acquire PET data, acquirePET data corresponding to the PET scanner 112/a detector unit of the PETscanner 112/a detector subunit of a detector unit, or the time when PETdata acquisition may occur. As another example, the control module 142may control processing module 144 to generate reconstruction data andselect different algorithms to process the reconstruction data for imagereconstruction. In some embodiments, the control module 142 may receivea real-time or a predetermined command provided by a user (e.g., adoctor) and adjust the acquisition module 141, and/or the processingmodule 144 to take images of a scan object according to the receivedcommand. In some embodiments, control module 142 may communicate withother modules in the imaging system 100 for exchanging information ordata.

The storage module 143 may store the acquired PET data, or the controlparameters, or the like, or a combination thereof. In some embodiments,the storage module 143 may include mass storage, removable storage,volatile read-and-write memory, read-only memory (ROM), or the like, orany combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage module 143 may store one or more programs and/orinstructions that may be executed by one or more processors of theimaging system 100 (e.g., processing module 144) to perform exemplarymethods described in this disclosure. For example, storage module 143may store program(s) and/or instruction(s) executed by the processor(s)of the imaging system 100 to acquire PET data, or reconstruct an imagebased on the PET data.

The processing module 144 may process data and/or information receivedfrom modules in the imaging system 100. In some embodiments, theprocessing module 144 may process PET data acquired by the acquisitionmodule 141, or retrieved from storage module 143. In some embodiments,the processing module 144 may reconstruct a PET image based on the PETdata, generate reports including one or more PET images and/or otherrelated information, or the like. For example, processing module 144 mayprocess the PET data based on a gating approach and reconstruct a PETimage based on the gated PET data.

It should be noted that the above description of the processing engine140 is merely provided for the purposes of illustration, and notintended to limit the scope of the present disclosure. In someembodiments, one or more modules illustrated in FIG. 1B may beimplemented in at least part of the exemplary imaging system 100illustrated in FIG. 1A. For example, at least two modules of theacquisition module 141, the control module 142, the storage module 143,and/or the processing module 144 may be integrated into a console. Viathe console, a user may set parameters for scanning, control the imagingprocedure, control a parameter of the reconstruction of an image, viewthe reconstructed images, etc. In some embodiments, the console may beimplemented via a host computer.

FIG. 2 is a block diagram illustrating exemplary hardware and softwarecomponents of computing device 200 on which the imaging system 100 maybe implemented according to some embodiments of the present disclosure.In some embodiments, the computing device 200 may include a processor202, a memory 204, and a communication port 206.

The processor 202 may execute computer instructions (program code) andperform functions of the processing module 144 in accordance withtechniques described herein. Computer instructions may include routines,programs, objects, components, data structures, procedures, modules, andfunctions, which perform particular functions described herein. Forexample, the processor 202 may process the data or information receivedfrom the acquisition module 141, the control module 142, the storagemodule 143, or any other component of imaging system 100. In someembodiments, the processor 202 may include a microcontroller, amicroprocessor, a reduced instruction set computer (RISC), anapplication specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof. For example, processor 202 may include amicrocontroller to process the PET data from the PET scanner 112 forimage reconstruction.

The memory 204 may store the data or information received from theacquisition module 141, the control module 142, the storage module 143,the processing module 144, or any other component of imaging system 100.In some embodiments, the memory 204 may include mass storage, removablestorage, volatile read-and-write memory, read-only memory (ROM), or thelike, or any combination thereof. For example, the mass storage mayinclude a magnetic disk, an optical disk, a solid-state drive, etc. Theremovable storage may include a flash drive, a floppy disk, an opticaldisk, a memory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the memory 204 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the memory 204 may store a program for the processing module144 for reconstructing a PET image based on the PET data acquired by thePET scanner 112 and/or the acquisition module 141.

The communication port 206 may transmit to and receive information ordata from any one of the acquisition module 141, the control module 142,the storage module 143, and the processing module 144. In someembodiments, the communication port 206 may include a wired port (e.g.,a Universal Serial Bus (USB) port, a High Definition MultimediaInterface (HDMI) port, or the like) or a wireless port (a Bluetoothport, an infrared interface, a WiFi port, or the like).

Merely for illustration, only one processor (the processor 202) isdescribed in the computing device 200. However, it should be noted thatthe computing device 200 in the present disclosure may also includemultiple processors, thus operations and/or method steps that areperformed by one processor as described in the present disclosure mayalso be jointly or separately performed by the multiple processors. Forpersons having ordinary skills in the art, multiple variations andmodifications may be made under the teaching of the present invention.However, those variations and modifications do not depart from the scopeof the present disclosure.

FIG. 3A is a schematic diagram illustrating a detector subunit accordingto some embodiments of the present disclosure. In some embodiments, adetector subunit 112A may include a plurality of PET detectors (e.g., aPET detector 301, a PET detector 302, a PET detector 303, etc.) anddetection circuits (not shown in the figure). The PET detectors mayfurther include a plurality of crystal elements (e.g., a crystal element301 a) and/or photomultiplier tubes (e.g., a photomultiplier tube 301b). In some embodiments, a crystal element may correspond to aphotomultiplier tube. The crystal element may emit visible light photonsafter an interaction with photons from radiation events originating froma scan object. The corresponding photomultiplier tube may amplify andconvert optical signals of the visible light photons into electricalsignals. The electrical signals may then be transmitted to detectioncircuits for subsequent processing. In some embodiments, aphotomultiplier tube applicable in the present disclosure may be asingle-channel photomultiplier tube or a multi-channel photomultipliertube. In some embodiments, the plurality of PET detectors may bearranged in a ring-shaped pattern, and an area surrounded by the PETdetectors may provide a detection region of the detector subunit 112A.

During a PET scan or analysis, a positron undergoes an annihilationevent 305 with an electron as the two particles collide. Theannihilation event 305 may result in two photons (e.g., gamma photons),and the two photons may begin to travel at (substantially) oppositedirections. One of the two photons may be counted by the PET detector302, and the other one may be counted by the PET detector 303. A singlephoton counted by a detector may be referred to as a “single event.” Theline connecting the two photons may be an LOR, and one or moreannihilation events may occur on the LOR. In some embodiments, PET datacollected by a detector subunit as used in the present disclosure mayinclude trajectory and/or information of photons detected by alldetectors in the detector subunit.

In some embodiments, multiple single events detected by the PET detectorin the detector subunit 112A may further be converted into one or morecoincident events. A coincident event may refer to two single eventscounted by two PET detectors respectively within a coincident timewindow (e.g., less than 10 ns, etc.) A coincident event may constitutetwo single events counted by the same detector subunit of a detectorunit. A coincident event may constitute two single events counted by thedifferent detector subunits of the same detector unit. A coincidentevent may constitute two single events counted by the different detectorsubunits of different detector units. In some embodiments, a coincidentevent may correspond to scattering coincidence, random coincidence, ortrue coincidence. A scattered coincidence may be one in which at leastone of the counted single event has undergone at least one Comptonscattering event before counted by a PET detector. A random coincidencemay occur when two photons arising from different annihilation eventswithin the coincident time window. A true coincidence event may includetwo single events in which photons derive from a singlepositron-electron annihilation counted by two detectors within thecoincident time window. In some embodiment, the true coincidence eventmay be determined by the coincidence detection including detectorsensitivity correction (normalization), isotope time decay correction,dead time correction, random coincidence correction, scatteringcoincidence correction, attenuation correction, geometric correction, orthe like, or a combination thereof.

FIG. 3B is a schematic diagram illustrating a detector unit according tosome embodiments of the present disclosure. The detector unit 112B mayinclude a detector subunit 311, a detector subunit 312, a detectorsubunit 313, a detector subunit 314, a detector subunit 315, a detectorsubunit 316, and a detector subunit 317. In some embodiments, thedetector subunits of the detector unit 112B may be arranged in a rowalong the z axis. An opening area formed by the detector subunits mayconstitute a detection region of the detector unit 112B. A scan objectmay be moved into and out of the detection region of a detector unitalong the z axis. The detector unit 112B illustrated in FIG. 3B may beformed by combining 7 ring-shaped detector subunits. A ring-shapeddetector subunit may be also referred to as a detector ring. It shouldbe noted that the above description of the PET unit is merely providedfor the purposes of illustration, more detector subunits (e.g., detectorrings) may be added to the PET unit.

In some embodiments, during a PET scan or analysis, a plurality ofannihilation photons may be counted by detectors of detector subunits ofthe detector unit 112B. The detector unit 112B may then generate PETdata based on a plurality of single events corresponding to theplurality of annihilation photons.

FIG. 3C is a schematic diagram illustrating a PET scanner according tosome embodiments of the present disclosure. The PET scanner 112 mayinclude a detector unit 320, a detector unit 330, a detector unit 340, adetector unit 350, a detector unit 360, a detector unit 370, a detectorunit 380, and a detector unit 390. A detector unit may include aplurality of detector subunits, as exemplified in FIG. 3B and thedescription thereof. In some embodiments, the plurality of detectorunits of the PET scanner 112 may be arranged in a row along the z axis.An opening area formed by the detector units may constitute a detectionregion of the PET scanner 112. A scan object may be moved into and outof the detection region of a PET scanner along the z axis. It should benoted that the above description of the PET scanner is merely providedfor the purposes of illustration, more detector units may be added tothe PET scanner.

In some embodiments, the plurality of detector units of the PET scanner112 may have a respective position label. A position label may representa position of a detector unit, relative to other detector units, in thePET scanner 112. An order of the plurality of detector units arranged inthe PET scanner 112 may be determined based on the respective positionlabels of the plurality of detector units. The PET scanner 112illustrated in FIG. 3C may be formed by combining 8 detector units thatare arranged along the z axis. The position labels of the eight detectorunits may be set as U1, U2, U3, U4, U5, U6, U7, and U8, respectively,according to where a detector unit is arranged, relative to the otherdetector units, in the PET scanner 112. Position label U1 may correspondto the detector unit 320. Position label U2 may correspond to thedetector unit 330. Position label U3 may correspond to the detector unit340. Position label U4 may correspond to the detector unit 350. Positionlabel U5 may correspond to the detector unit 360. Position label U6 maycorrespond to the detector unit 370. Position label U7 may correspond tothe detector unit 380. Position label U8 may correspond to the detectorunit 390.

In some embodiments, during a PET scan or analysis, a plurality ofannihilation gamma photons may be counted by detectors of the detectorunits of the PET scanner 112. The PET scanner 112 may then generate PETdata based on a plurality of single events corresponding to theplurality of annihilation gamma photons. In some embodiments, PET datacollected by the PET scanner 112 (also referred to as “the entire PETdata”) as used in the present disclosure may include data of multiplesingle events detected by all detector units in the PET scanner. In someembodiments, the entire PET data or some of the entire PET data may beused to determine a distribution of the PET tracer molecules in theimage domain and/or the coincidence distribution of voxels in thesinogram coordinate system.

Any two detector units corresponding to a coincident event may bereferred to as a “detector unit pair.” A detector unit pair may countone or more coincident events. In the present disclosure, a detectorunit pair may be denoted by the position labels of the detector units.For example, a coincidence event may be counted by a PET detectorlocated in the detector unit 330 with position label U2 and a PETdetector located in the detector unit 340 with position label U3, andthe combination of detector unit 330 and detector unit 340 may bereferred as a detector unit pair. As another example, a coincidenceevent may be counted by PET detectors both located in the detector unit330 alone, and the combination of detector unit 330 and detector unit330 may be referred as a detector unit pair. A unit difference mayrepresent a position difference between the two detector units in adetector unit pair. For example, the unit difference of a detector unitpair including detector unit 330 and detector unit 350 may be twoaccording to the position labels. As another example, the unitdifference of a detector unit pair including detector unit 330 alone maybe zero. The unit difference may also represent an axial distancebetween the two detector units of a detector unit pair. For example, theunit difference of the detector unit pair including detector unit 330and detector unit 350 may be a distance from the center of detector unit330 to the center of detector unit 350. In some embodiments, the PETdata used in image reconstruction may be part of the acquired PET data.A PET data selection may be performed according to a rule. For instance,if the unit difference of a detector unit pair exceeds a threshold, acoincidence event counted by the detector unit pair may be not used inimage reconstruction due to factors including, for example, travelingattenuation of the photons.

It should be noted that the above description of the detector, detectorsubunit, and detector unit is merely provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. For example, more detectors may be added to adetector subunit, and detectors of a detector subunit may be implementedin any suitable manner, e.g., a ring, a rectangle, or an array. Asanother example, more detector subunits may be added to a detector unit,and an opening area formed by detector subunits may constitute adetection region of a detector unit. As a further example, more detectorunits may be added to a PET scanner, and a chance of single events to becounted may increase.

FIG. 4 is a block diagram of an exemplary processing module according tosome embodiments of the present disclosure. The processing module 144may include a functional status determination unit 410, a reconstructiondata generation unit 420, and an image reconstruction unit 430. In someembodiments, the units may be connected with each other via a wiredconnection (e.g., a metal cable, an optical cable, a hybrid cable, orthe like, or any combination thereof) or a wireless connection (e.g., aLocal Area Network (LAN), a Wide Area Network (WAN), a Bluetooth, aZigBee, a Near Field Communication (NFC), or the like, or a combinationthereof).

The functional status determination unit 410 may determine functionalstatus of a detector unit of the PET scanner 112. In some embodiments,the functional status of a detector unit may be assessed based on one ormore performance parameters via the functional status determination unit410. The functional status determination unit 410 may generate aperformance parameter of a detector unit in the PET scanner 112real-time. Exemplary performance parameters of a detector unit mayinclude a counting rate. As used herein, a counting rate may be equal tothe number (or counts) of single events counted by the detector unit perunit time, e.g., one second. The counting rate for a detector unit maybe determined by dividing the counts of single events counted by thedetector unit in a certain time period (e.g., 10 seconds) by the certaintime period. For example, a counting rate of a detector unit obtainedreal-time may be determined by or transmitted to the functional statusdetermination unit 410, and may be compared with a threshold by thefunctional status determination unit 410. In some embodiments, when acounting rate of a detector unit is below a threshold, the functionalstatus determination unit 410 may designate the detector unit asdefective or non-functional, indicating that the detector unit is undera negative status (e.g., abnormal working status). When a counting rateof a detector unit equals or exceeds a threshold, the functional statusdetermination unit 410 may designate the detector unit functional,indicating that the detector unit is under a positive status (e.g.,normal working status).

In some embodiments, the functional status determination unit 410 maydetermine functional status of a detector unit based on a functionalstatus of a detector subunit. The functional status determination unit410 may determine the functional status of a detector subunit based onthe counting rate of a detector subunit. The counting rate for adetector subunit may be determined by dividing the counts of singleevents counted by the detector subunit in a certain time period (e.g.,10 seconds) by the certain time period. When a counting rate of adetector subunit is below a threshold, the functional statusdetermination unit 410 may designate the detector subunit as defectiveor non-functional, indicating that the detector unit including thedefective detector subunit is in a negative status (e.g., abnormalworking status).

In some embodiments, the functional status determination unit 410 maydetermine the functional status of a detector subunit of a detector unitbased on the performance parameter. In some embodiments, if a detectorsubunit of a detector unit is determined to be under a negative status,the detector unit including the non-functional detector subunit may bedesignated as a non-functional unit.

The reconstruction data generation unit 420 may determine reconstructiondata based on the current functional status of respective detector unitsand the PET data acquired by the corresponding detector units. Thereconstruction data may be used to perform PET image reconstruction. Thecurrent functional status of a detector unit may be determined by thefunctional status determination unit 410. The PET data collected by therespective detector units may be obtained by the acquisition module 141.In some embodiments, the reconstruction data generation unit 420 maydetermine reconstruction data based on the PET data collected by aplurality of detector units according to the current functional statusof the respective detector units. When there is no non-functionaldetector unit in the PET scanner, the acquisition module 141 may be setto a full data collection mode, and detector units (detector subunits)may operate in a full operation mode. Under the full data collectionmode, the acquisition module 141 may collect PET data detected by alldetector units. Under the full operation mode, all detector units (orall detector subunits) in the PET scanner may be allowed to operate. Thereconstruction data generation unit 420 may determine the reconstructiondata based on the PET data of all detector units acquired by theacquisition module 141.

When the functional status determination unit 410 determines that thereis a non-functional detector unit or a non-functional detector subunitin the PET scanner before or during a PET scan, a data collection modefor generating reconstruction data may be selected. In some embodiments,the acquisition module 141 may be set to a partial data collection modewhile detector units (detector subunits) are allowed to maintain at thefull operation mode. Under the partial data collection mode, only aportion of the collected PET data may be used for further processing.For instance, PET data collected by the non-functional detector unit ornon-functional detector subunit may be excluded, and PET data collectedby a functional detector unit or functional detector subunit may bepreserved for further processing. Under the full operation mode, alldetector units (or all detector subunits) in the PET scanner, includingthe functional ones and the non-functional ones, may be allowed tooperate. The reconstruction data generation unit 420 may generate thereconstruction data for performing PET image reconstruction based on thepreserved PET data collected by the functional detector unit(s) orfunctional detector subunit(s).

In some embodiments, the acquisition module 141 may be set to a fulldata collection mode, and detector units (detector subunits) may operatein a partial operation mode. Under the partial operation mode,non-functional detector unit(s) may be shut down by, e.g., the controlmodule 142, while a detector unit including a non-functional detectorsubunit is designated as a non-functional detector unit. Under the fulldata collection mode, all collected PET data may be used for furtherprocessing. For instance, the PET scanner is set to a partial operationmode in which all non-functional detector unit(s) are shut down, andonly the functional detector units are allowed to operation, PET datacollected by all functional detector unit(s) may be obtained andpreserved for further processing. The reconstruction data generationunit 420 may generate the reconstruction data based on all PET data offunctional detector unit(s) acquired by the acquisition module 141, eachof which does not include a non-functional detector subunit.

When one or more non-functional detector units are identified in the PETscanner, the reconstruction data may be determined based on PET datacollected by the functional detector units. In some embodiments, thereconstruction data may include PET data collected by functionaldetector units in a subgroup that are located together and not separatedby a non-functional detector unit. In some embodiments, if there aremore than one subgroups containing functional detector unit(s) that arelocated together and not separated by a non-functional detector unit,the reconstruction data may include PET data collected by the subgroupincluding more functional detector units.

In some embodiments, when the number of detector units in the subgroupthat includes more functional detector units is below a threshold, anoperation of data compensation may be performed. The data compensationmay be performed mathematically according to an algorithm. Exemplaryalgorithms may include a closest element algorithm, a bilinearinterpolation algorithm, a cubic interpolation algorithm, etc. In someembodiments, the data compensation may also be implemented by performinganother PET scan as directed by, e.g., the control module 142. Moredescriptions may be found in FIG. 7A and FIG. 7B, and descriptionsthereof.

The image reconstruction unit 430 may reconstruct a PET image based onthe reconstruction data. The reconstruction data may be generated by thereconstruction data generation unit 420. In some embodiments, the imagereconstruction unit 430 may employ different kinds of imagingreconstruction techniques for image reconstruction. Exemplary imagereconstruction techniques may include Fourier reconstruction,constrained image reconstruction, regularized image reconstruction, orthe like, or a variation thereof, or a combination thereof. In someembodiments, the image reconstruction unit 430 may use differentreconstruction algorithms including an analytic reconstruction algorithmor an iterative reconstruction algorithm for image reconstruction.Exemplary analytic reconstruction algorithms may include a filter backprojection (FBP) algorithm, a back projection filter (BFP) algorithm, aρ-filtered layer gram, or the like, or a combination thereof. Exemplaryiterative reconstruction algorithms may include a Maximum LikelihoodExpectation Maximization (ML-EM), an Ordered Subset ExpectationMaximization (OSEM), a Row-Action Maximum Likelihood Algorithm (RAMLA),a Dynamic Row-Action Maximum Likelihood Algorithm (DRAMA), or the like,or a combination thereof.

In some embodiments, when a CT-PET multi-modality system is used, theimage reconstruction unit 430 may reconstruct a CT image based on CTscanning data to display the shape and/or position of a scan object.Furthermore, the CT scanning data or the CT image may be used forattenuation correction of a PET or SPET scan. In some embodiments, whena PET scan is implemented by scanning one or more scan regions of thescan object, the image reconstruction unit 430 may include stitching PETsub-images of the one or more scan regions of the scan object togenerate a PET image of the scan object.

It should be noted that the above description of the processing module144 is merely provided for the purposes of illustration, and notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made under the teachings of the present disclosure. For instance,the assembly and/or function of the processing module 144 may be variedor changed according to specific implementation scenarios. Merely by wayof example, some other components may be added into the processingmodule 144, such as a position label setting unit, an image/data outputunit, and other units.

FIG. 5 is a flowchart illustrating an exemplary process 500 ofreconstructing a PET image according to some embodiments of the presentdisclosure. In some embodiments, one or more operations of the process500 illustrated in FIG. 5 for PET image reconstruction may beimplemented in the imaging system 100 illustrated in FIG. 1A. Forexample, the process 500 illustrated in FIG. 5 may be stored in thestorage 150 in the form of instructions, and invoked and/or executed bythe processing engine 140 (e.g., the processor of a computing device).As another example, a portion of the process 500 may be implemented onthe PET scanner 112.

In 510, PET data relating to a scan object collected by a plurality ofdetector units may be obtained. The PET data relating to a scan objectcollected by a plurality of detector units may be obtained via theacquisition module 141. In some embodiments, PET data relating to a scanobject collected by a plurality of detector units may be obtained fromthe storage module 143. The PET data collected by a plurality ofdetector units may further be used to determine the distribution of thePET tracer molecules in the image domain and/or the coincidencedistribution of voxels in the sinogram coordinate system. The PET datamay include one or more coincidence events counted by one or moredetector unit pairs.

In 520, the current functional status of each of the plurality ofdetector units may be determined. The current functional status of theplurality of detector units may be determined by the functional statusdetermination unit 410. In some embodiments, the functional statusdetermination unit 410 may determine the current functional status ofthe plurality of detector units based on one or more performanceparameters. The functional status determination unit 410 may generateone or more performance parameters of the detector units in the PETscanner 112 real-time. Exemplary performance parameters of a detectorunit may include a counting rate. The counting rate for a detector unitmay be determined by dividing the counts of single events counted by thedetector unit in a certain time period by the certain time period. Forexample, a counting rate of a detector unit obtained real-time may bedetermined by or transmitted to the functional status determination unit410, and may be compared with a threshold by the functional statusdetermination unit 410. In some embodiments, when a counting rate of adetector unit is below a threshold, the detector unit may be designatedas a non-functional unit by the functional status determination unit410, indicating that the detector unit is under a negative status (e.g.,abnormal working status). When a counting rate of a detector unit equalsor exceeds a threshold, the detector unit may be designated as afunctional unit by the functional status determination unit 410,indicating that the detector unit is under a positive status (e.g.,normal working status).

In 530, reconstruction data may be determined based on the currentfunctional status of respective detector units and the PET data acquiredby the corresponding detector units. The reconstruction data may be usedto perform PET image reconstruction. The current functional status ofcorresponding detector unit may be determined by the functional statusdetermination unit 410. The PET data collected by the respectivedetector units may be obtained by the acquisition module 141. In someembodiments, PET data collected by a non-functional detector unit ordetector subunit may be removed and not used to perform imagereconstruction. In some embodiments, the reconstruction data generationunit 420 may determine reconstruction data based on the PET datacollected by a plurality of detector units according to the currentfunctional status of the respective detector units. The reconstructiondata may include the PET data acquired by the functional detector units,each of which does not include a non-functional detector subunit.

In 540, a PET image may be reconstructed based on the reconstructiondata. Image reconstruction of the PET image may be implemented by theimage reconstruction unit 430. The reconstruction data may be determinedby the reconstruction data generation unit 420. In some embodiments, anyone of different kinds of imaging reconstruction techniques for imagereconstruction may be used to reconstruct a PET image. Exemplary imagereconstruction techniques may include Fourier reconstruction,constrained image reconstruction, regularized image reconstruction inparallel MRI, or the like, or a variation thereof, or a combinationthereof. In some embodiments, different reconstruction algorithmsincluding an analytic reconstruction algorithm or an iterativereconstruction algorithm for image reconstruction may be used. Exemplaryanalytic reconstruction algorithms may include a filter back projection(FBP) algorithm, a back projection filter (BFP) algorithm, a ρ-filteredlayer gram, or the like, or a combination thereof. Exemplary iterativereconstruction algorithms may include a Maximum Likelihood ExpectationMaximization (ML-EM), an Ordered Subset Expectation Maximization (OSEM),a Row-Action Maximum Likelihood Algorithm (RAMLA), a Dynamic Row-ActionMaximum Likelihood Algorithm (DRAMA), or the like, or a combinationthereof.

It should be noted that the above description of the process ofreconstructing a PET image is merely provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. For example, process 500 may further include anoperation of storing the PET data relating to a scan object collected bya plurality of detector units in the storage 150. As another example,process 500 may further include an operation of outputting the PETimage. Such variations and modifications do not depart from the scope ofthe present disclosure.

FIG. 6 is a flowchart illustrating an exemplary process of determiningreconstruction data according to some embodiments of the presentdisclosure. In some embodiments, one or more operations of the process600 illustrated in FIG. 6 for determining reconstruction data may beimplemented in the imaging system 100 illustrated in FIG. 1A. Forexample, the process 600 illustrated in FIG. 6 may be stored in thestorage 150 in the form of instructions, and invoked and/or executed bythe processing engine 140 (e.g., the processor of a computing device).

In 610, the reconstruction data generation unit 420 may assign one ormore detector units into different groups based on functional status ofthe one or more detector units. The reconstruction data generation unit420 may assign one or more functional units into a first group and oneor more non-functional detector unit into a second group. In someembodiments, the reconstruction data generation unit 420 may determinereconstruction data based on coincidence events counted by the detectorunit(s) in the first group. In some embodiments, the first group mayinclude one or more subgroups containing functional detector unit(s).

In 620, the reconstruction data generation unit 420 may further assignone or more functional detector units into different subgroups based onpositions of the one or more detector units in the first group. Thereconstruction data generation unit 420 may assign one or more detectorunit(s) that are located together and not spatially separated by anon-functional detector unit into a subgroup. Two subgroups offunctional detector units may be separated by one or more non-functionaldetector unit. In some embodiments, if a functional detector unit A isnot adjacent to any other functional detector unit in the first group,the reconstruction data generation unit 420 may assign the functionaldetector unit A into a subgroup only containing the functional detectorunit A.

In 630, the reconstruction data generation unit 420 may determine atarget subgroup based on a rule. The rule may include comparing detectorunit number of the one or more subgroups, and determine a subgroup asthe target subgroup based on unit number of the different subgroups. Insome embodiments, the reconstruction data generation unit 420 maydetermine reconstruction data based on coincidence events counted by thedetector unit(s) of a subgroup that includes the most functionaldetector units than any other subgroup(s) in the first group.

In 640, the reconstruction data generation unit 420 may determinereconstruction data based on the PET data collected by the detectorunit(s) in the target subgroup. PET data may include single eventscounted by the detector unit(s) in the target subgroup. Thereconstruction data may be determined based on coincidence eventscounted by one or more detector unit pairs. The reconstruction datageneration unit 420 may generate reconstruction data based oncoincidence events counted by one or more detector unit pairs belongingto detector unit(s) in the target subgroup.

It should be noted that the above description of the process ofdetermining reconstruction data is merely provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, multiplevariations and modifications may be made under the teachings of thepresent disclosure. For example, process 600 may further include anoperation in which the functional status of respective detector unitsmay be recorded by the computing device 200. As another example, process600 may further include an operation in which the PET scanner 112 may beshut down when the unit number of the target subgroup is below athreshold, e.g., 2. Such variations and modifications do not depart fromthe scope of the present disclosure.

FIG. 7A illustrates an exemplary PET scanner with a non-functionaldetector unit. As shown in FIG. 7A, the PET scanner 112 may includedetector unit 320 labeled as U1, detector unit 330 labeled as U2,detector unit 340 labeled as U3, detector unit 350 labeled as U4,detector unit 360 labeled as U5, detector unit 370 labeled as U6,detector unit 380 labeled as U7, and detector unit 390 labeled as U8.FIG. 7B illustrates a map of detector unit pairs that detect coincidentevents. For example, C (1, 1) may represent one or more coincidenceevents counted by detector unit 320 and detector unit 320, and C (1, 2)may represent one or more coincidence events counted by detector unit320 and detector unit 330, and so on. As shown in FIG. 7A, detector unit360 is determined as a non-function unit by the functional statusdetermination unit 410, while the rest of the detector units aredetermined as functional units. The reconstruction data generation unit420 may assign functional detector units into a first group, includingdetector unit 320, detector unit 330, detector unit 340, detector unit350, detector unit 370, detector unit 380, and detector unit 390. Thereconstruction data generation unit 420 may assign non-functionaldetector units into a second group, including detector unit 360. Thereconstruction data generation unit 420 may further assign the detectorunits in the first group into subgroups based on their locationsrelative to the location of the non-functional detector unit 360. Thereconstruction data generation unit 420 may further assign detector unit320, detector unit 330, detector unit 340, and detector unit 350 into afirst subgroup because these detector units are located together and notspatially separated by the non-functional detector unit 360. Thereconstruction data generation unit 420 may assign detector unit 370,detector unit 380, and detector unit 390 into a second subgroup becausethese detector units are located together and not spatially separated bythe non-functional detector unit 360. The reconstruction data generationunit 420 may determine the number of detector units of in each of theone or more subgroups, compare the numbers to identify the targetsub-group that includes the most functional detector units that arelocated together and not spatially separated by the non-functionaldetector unit 360. Then the reconstruction data generation unit 420 maydetermine reconstruction data based on coincidence events counted by thedetector unit(s) in the target subgroup. As illustrated, the firstsubgroup includes four functional detector units and the second subgroupincludes three functional detector units. The reconstruction datageneration unit 420 may determine reconstruction data based oncoincidence events counted by detector unit pairs in the first subgroup,including C(1, 1), C(1, 2), C(1, 3), C(1, 4), C(2, 2), C(2, 3), C(2, 4)C(3, 3) C(3, 4), and C(4, 4).

In some embodiments, a scan object may be divided into one or more scanregions. When the table 114 is positioned at a table position, each ofthe one or more scan regions may correspond to at least one detectorunit. A position of each of the one or more scan regions on the table114 may also correspond to a spatial position of respective detectorunits. Coincidence events relating to a scan region corresponding to anon-functional detector unit (e.g., the detector unit 360) may bereplaced by other coincidence events relating to the scan region countedby one or more other functional detector units in another PET scanperformed when the table 114 is moved to a different table position. Forexample, by moving the table 114 or the PET scanner 112 along the zaxis, the scan region corresponding to a non-functional detector unitmay be moved to a position that corresponds to one or more functionaldetector units. The scan region now corresponding to a functionaldetector unit may then be scanned. PET data relating to the scan regionmay be obtained using the functional detector unit(s), and may replacethe PET data relating to the scan region obtained in the previous PETscan.

For example, as shown in FIG. 7A, in a first PET scan, detector unit 360is determined as a non-function detector unit, and a position of thetable 114 may be referred to as table position 1 at this time. The tableposition may refer to a spatial position of the table 114. In someembodiments, a table position may be defined or described relative to animmobile portion, e.g., an immobile portion of the imaging section 110(e.g., the detection region 113 of the imaging section 110), an immobileportion of the table 114 (e.g., an immobile base of the table 114), thefloor on which the imaging section 110 or the table 114 sits.Coincidence events relating to a scan region corresponding to detectorunit 360 may be excluded from the reconstruction data, including C(5,5),C(4,5), C(3,5), C(2,5), C(1,5). Then in a second PET scan, by moving thetable 114 from the table position 1 to a table position 2, the scanregion corresponding to detector unit 360 may be moved to a positionthat corresponding to detector unit 350, which is a functional detectorunit. The table position 2 may be a position of table 114 when the scanregion is corresponding to detector unit 350. Thus, coincidence eventscounted by the detector unit 330 and 340 in the second PET scan mayreplace coincidence event counted by the PET 350 and 340 (e.g. C(4, 5)).Coincidence events counted by the detector unit 320 and 340 in thesecond PET scan may replace coincidence event counted by the PET 350 and330 (e.g. C(3, 5)). Coincidence events counted by the detector unit 310and 340 in the second PET scan may replace coincidence event counted bythe PET 350 and 320 (e.g. C(2, 5)). Based on a scan of each of the oneor more scan regions by one or more function detector units,reconstruction data relating to the scan object may be obtained byperforming data reorganization on coincidence events (also referred toas PET data) relating to the one or more scan regions. A PET image ofthe scan object may be obtained based on the reconstruction data.

FIG. 8A illustrates another exemplary PET scanner with non-functionaldetector units. FIG. 8B illustrates a map of detector unit pairs thatdetect coincident events. For example, C (6, 6) may represent one ormore coincidence events counted by detector unit 370 and detector unit370, and C (6, 7) may represent one or more coincidence events countedby detector unit 370 and detector unit 380, and so on.

As shown in FIG. 8A, detector unit 340 and detector unit 360 may bedetermined as non-function units by the functional status determinationunit 410, while the rest of the detector units may be determined asfunctional units. The reconstruction data generation unit 420 may assignfunctional detector units into a first group, including detector unit320, detector unit 330, detector unit 350, detector unit 370, detectorunit 380, and detector unit 390. The reconstruction data generation unit420 may assign non-functional detector units into a second group,including detector unit 340 and detector unit 360.

The reconstruction data generation unit 420 may further assign thedetector units in the first group into subgroups based on theirlocations relative to the location of the non-functional detector unit340 and 360. The reconstruction data generation unit 420 may furtherassign detector unit 320 and detector unit 330 into a first subgroupbecause these detector units are located together and not spatiallyseparated by the non-functional detector unit 340 or 360. Thereconstruction data generation unit 420 may assign detector unit 370,detector unit 380, and detector unit 390 into a second subgroup becausethese detector units are located together and not spatially separated bythe non-functional detector unit 340 or 360. The reconstruction datageneration unit 420 may then assign detector unit 350 into a thirdsubgroup because detector unit 350 is not adjacent to any otherfunctional detector unit in the first group.

The reconstruction data generation unit 420 may determine the number ofdetector units of in each of the one or more subgroups, compare thenumbers to identify the target subgroup that includes the mostfunctional detector units that are located together and not spatiallyseparated by the non-functional detector unit 340 or 360. Then thereconstruction data generation unit 420 may determine reconstructiondata based on coincidence events counted by the detector unit(s) in thetarget subgroup. As illustrated, the first subgroup includes twofunctional detector units, the second subgroup includes three functionaldetector units, and the third subgroup including one functional detectorunit. The reconstruction data generation unit 420 may determinereconstruction data based on coincidence events counted by detector unitpairs in the second subgroup, including C (6, 6), C (6, 7), C (6, 8), C(7, 7), C (7, 8), and C (8, 8), as shown in FIG. 8B.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A non-transitory computer readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectro-magnetic, optical, or the like, or any suitable combinationthereof. A computer readable signal medium may be any computer readablemedium that is not a computer readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran 2003, Perl,COBOL 2002, PHP, ABAP, dynamic programming languages such as Python,Ruby and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate,” or “substantially” may indicate ±20% variationof the value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

We claim:
 1. A method for an imaging device, wherein the imaging devicecomprise a processor and a plurality of detector units, the methodcomprising: obtaining, by the processor, first scan data relating to anobject, wherein the first scan data is collected by the plurality ofdetector units; dividing, by the processor, the plurality of detectorunits into non-functional detector units and functional detector unitsbased on the first scan data; determining, by the processor, whether acount of the non-functional detector units is greater than a threshold;and in response to determining that the count of the non-functionaldetector units is greater than the threshold, reconstructing an image bya first process including: determining, by the processor, one or morescan regions of the object corresponding to the non-functional detectorunits; obtaining, by the processor, second scan data relating to the oneor more scan regions of the object, wherein the second scan data iscollected by the functional detector units; determining, by theprocessor, reconstruction data based on the second scan data and a partof the first scan data; and reconstructing, by the processor, the imagebased on the reconstruction data.
 2. The method of claim 1 furthercomprising: in response to determining that the count of thenon-functional detector units is not greater than the threshold,reconstructing the image by a second process including: determining, bythe processor, reconstruction data based on the first scan data; andreconstructing, by the processor, the image based on the reconstructiondata.
 3. The method of claim 1, wherein the part of the first can datais data collected by the functional detector units.
 4. The method ofclaim 1, further comprising: determining, by the processor, one or moreunit pairs from the plurality of detector units; determining, by theprocessor, a unit difference for each of the one or more unit pairs;comparing, by the processor, the unit difference with a unit differencethreshold; and determining, by the processor, the reconstruction databased on a result of the comparison.
 5. The method of claim 1, wherein adetector unit of the plurality of detector units includes one or morePET detector rings.
 6. The method of claim 1, wherein the first scandata is PET data or CT data.
 7. The method of claim 1, wherein thenon-functional detector units and functional detector units are dividedbased on counting rates for the plurality of detector units.
 8. Themethod of claim 1, wherein a detector unit of the plurality of detectorunits includes a plurality of detector subunits, and dividing theplurality of detector units into non-functional detector units andfunctional detector units based on the first scan data furthercomprising: for each detector unit of the plurality of detector units,determining functional status of respective detector subunits of adetector unit; and determining functional status of the detector unitbased on the functional status of the plurality of detector subunits ofthe detector unit.
 9. The method of claim 1, wherein the determining, bythe processor, one or more scan regions of the object corresponding tothe non-functional detector units includes: generating the one or morescan regions of the object by dividing a volume of interest of theobject.
 10. The method of claim 1, wherein at least a portion of thefunctional detector units are located side by side.
 11. A systemcomprising: at least one storage device including a set of instructions;and at least one processor configured to communicate with the at leastone storage device, wherein when executing the set of instructions, thesystem is configured to perform operations including: obtaining firstscan data relating to an object, wherein the first scan data iscollected by a plurality of detector units; dividing the plurality ofdetector units into non-functional detector units and functionaldetector units based on the first scan data; determining whether a countof the non-functional detector units is greater than a threshold; and inresponse to determining that the count of the non-functional detectorunits is greater than the threshold, reconstructing an image by a firstprocess including: determining one or more scan regions of the objectcorresponding to the non-functional detector units; obtaining secondscan data relating to the one or more scan regions of the object,wherein the second scan data is collected by the functional detectorunits; determining reconstruction data based on the second scan data anda part of the first scan data; and reconstructing the image based on thereconstruction data.
 12. The system of claim 11, wherein the system isfurther configured to perform the operations including: in response todetermining that the count of the non-functional detector units is notgreater than the threshold, reconstructing the image by a second processincluding: determining reconstruction data based on the first scan data;and reconstructing the image based on the reconstruction data.
 13. Thesystem of claim 11, wherein the part of the first scan data is datacollected by the functional detector units.
 14. The system of claim 11,wherein the system is further configured to perform the operationsincluding: determining one or more unit pairs from the plurality ofdetector units; determining a unit difference for each of the one ormore unit pairs; comparing the unit difference with a unit differencethreshold; and determining the reconstruction data based on a result ofthe comparison.
 15. The system of claim 11, wherein a detector unit ofthe plurality of detector units includes one or more PET detector rings.16. The system of claim 11, wherein the first scan data is PET data orCT data.
 17. The system of claim 11, wherein the non-functional detectorunits and functional detector units are divided based on counting ratesfor the plurality of detector units.
 18. The system of claim 11, whereina detector unit of the plurality of detector units includes a pluralityof detector subunits, and the dividing the plurality of detector unitsinto non-functional detector units and functional detector units basedon the first scan data includes: for each detector unit of the pluralityof detector units, determining functional status of respective detectorsubunits of a detector unit; and determining functional status of thedetector unit based on the functional status of the plurality ofdetector subunits of the detector unit.
 19. The system of claim 11,wherein the determining one or more scan regions of the objectcorresponding to the non-functional detector units includes: generatingthe one or more scan regions of the object by dividing a volume ofinterest of the object.
 20. The system of claim 11, wherein at least aportion of the functional detector units are located side by side.